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Abstract

This paper explores the planning and control
of a manipulation task accomplished in conditions of high
uncertainty. Statistical techniques, like particle ﬁlters, provide a
framework for expressing the uncertainty and partial observability of the real world and taking actions to reduce them.
We explore a classic manipulation problem of planar batting,
but with a new twist of shape, pose and impact uncertainty.
We demonstrate a technique for characterizing and reducing
this uncertainty using a particle ﬁlter coupled with a lookahead
planner that maximizes information gain. We show that a twostep planner that ﬁrst acts for information gain and then acts to
maximize the expectation of achieving a desired goal is effective
at managing shape, pose and impact uncertainty